Vehicle identification system and method

ABSTRACT

The system and method are configured to identify cars and obtain information about the identified cars. The system and method involves a software application installed on a computing device that includes a camera configured to capture images. After the software application is installed, the user can activate the camera through the software application and point the camera at a vehicle. The software application includes an object recognition function that can recognize the vehicle in the view of the camera. After recognizing the vehicle, the software application displays information about vehicle on a screen of the computing device. The software application can be used with an event such as an auto show. In addition to identifying the cars, the identified information can be used for other purposes such as a part of a scavenger hunt or other contests. Awards can be given to participants who complete the contest.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application is a continuation of U.S. application Ser. No.16/740,364 filed Jan. 10, 2020, which claims the benefit of U.S.Provisional Application 62/791,707, filed Jan. 11, 2019, the entirety ofeach which are herein incorporated by reference.

FIELD OF THE INVENTION

The present invention is related to systems and methods for identifyingobjects in an image. More particularly, the systems and methods involveartificial neural networks for mobile and embedded vision applicationsand reducing computational complexity.

BACKGROUND OF THE INVENTION

Technologies for identifying objects in an image are known in the art.Those technologies employ machine learning systems that arecomputationally intensive. Conventional machine learning systems usetraditional convolution techniques that require processing a largenumber of parameters. The layers in the neural network include manynodes, labels, weights, and other parameters connected together. Theconnected nodes, labels, weights, and other parameters are known as agraph or graphs. Traditional convolution techniques methods need toanalyze every or almost every parameter in the graph and perform aseries of mathematical operations (e.g., addition, subtraction,multiplication, and division). These analysis and operations, orfloating point multiplication operations, demand substantial computingpower.

Moreover, conventional machine learning systems are configured toperform general tasks (e.g., identifying whether the object in the imageis a cat, dog, car, boat, airplane, etc.) and may not be accurate inperforming specific tasks (e.g., identifying whether the object in theimage is a particular type of car, such as a Toyota Camry, Honda Accord,Ford Taurus, etc.). Training such systems are also complicated and timeconsuming because they require using a very large set of images andtraining the entire system.

There is a need in the art for improved systems and methods forprocessing images and identifying objects in the images.

SUMMARY OF THE INVENTION

In accordance with principles of the invention, a computer-implementedmethod for identifying a vehicle is contemplated. The method comprisesimplementing a software application on a mobile device configured toidentify vehicles in an auto show event and to obtain information aboutvehicles and unlock prizes. The execution of the software applicationallows a processor and memory of the mobile device to: implement a firstdatabase including target vehicles in the auto show event; implement asecond database including prize information; activate a camera of themobile device; identify a vehicle in the view of the camera, wherein thestep of identifying includes recognizing that the vehicle in the viewmatches or closely matches a target vehicle stored in the first databaseand displaying vehicle information of the target vehicle when theidentified vehicle matches or closely matches the target vehicle on ascreen of the mobile device; identify the target vehicle to which thevehicle in the view of the camera matches or closely matches as anunlocked vehicle and track a number of unlocked vehicles; and presentprize information from the second database on the screen of the mobiledevice according to the number of unlocked vehicles.

In one embodiment, information of each of the target vehicles stored inthe first database is inaccessible to user of the software applicationbefore the target vehicle is identified by the software application asan unlocked vehicle.

In one embodiment, the execution of the software application allows aprocessor and memory of the mobile device to further implement a controlinterface configured to control user access to the information of eachof the target vehicles stored in the first database. The controlinterface is configured to require user of the software application toperform additional interactions with the software application after thesoftware application recognizing that the vehicle in the view of thecamera matches or closely matches a target vehicle stored in the firstdatabase in order to identify the target vehicle as unlocked vehicle.

In one embodiment, the execution of the software application allows aprocessor and memory of the mobile device to further compare the numberof unlocked vehicles with another number of unlocked vehicles tracked bythe software application implemented on another mobile device. Theexecution of the software application allows a processor and memory ofthe mobile device to further present prize information from the seconddatabase on the screen of the mobile device based on the step ofcomparison.

In one embodiment, the execution of the software application allows aprocessor and memory of the mobile device to further present prizeinformation from the second database on the screen of the mobile devicewhen a specific target vehicle is identified as unlocked.

In one embodiment, the step of identifying a vehicle is executed by amachine learning machine. The machine learning machine includes adetection component of a pre-trained neural network, wherein thedetection component is configured to detect patterns, edges, shapes, andother characteristics of an object in a set of images containing theobject, wherein the object is not a vehicle; and a neural networkconfigured to receive another set of images that each contains a vehicleand relied on the detection component to learn that each image containsthe vehicle.

In one embodiment, the step of identifying a vehicle is executed by amachine learning machine. The machine learning machine includes a firstartificial neural network configured to receive the frame, detectfeatures of the object in the frame, and produce a features vectorrepresentative of the detected features; and a second artificial neuralnetwork configured to receive the features vector and classify thefeatures vector to one of the category labels implemented on the secondartificial neural network. The machine learning system is configured toidentify the object in the frame based on the category label to whichthe features vector is classified.

In one embodiment, the step of identifying a vehicle is executed by amachine learning machine. The machine learning machine includes a firstartificial neural network configured to receive the frame, detectfeatures of the vehicle in the frame, and produce a features vectorrepresentative of the detected features, wherein the first artificialneural network is a feature learning neural network from a general taskartificial neural network that has been trained with images from a largedataset, wherein the images from the large dataset include no images ofthe target vehicles; and a second artificial neural network configuredto receive the features vector and classify the features vector to oneof the category labels implemented on the second artificial neuralnetwork, wherein the second artificial neural network is an imageclassification neural network from a custom neural network and the imageclassification neural network has been trained using the featurelearning neural network from a general task artificial neural networkand with images from a small dataset, wherein the images from the smalldataset include images of the target vehicles.

Counterpart system and computer-readable medium embodiments would beunderstood from the overall disclosure. Also, broader, narrower, ordifferent combinations of the described features are contemplated, suchthat, for example, features can be removed or added in a broader ornarrower way.

BRIEF DESCRIPTION OF THE DRAWINGS

The nature and various advantages of the present invention will becomemore apparent upon consideration of the following detailed description,taken in conjunction with the accompanying drawings, in which likereference characters refer to like parts throughout, and in which:

FIG. 1 depicts part of the user interface of the software application inaccordance with some embodiments of the present invention;

FIG. 2 depicts an illustrative operation of (or user interaction with)the software application in accordance with some embodiments of thepresent invention;

FIG. 3 depicts another illustrative operation of (or user interactionwith) the software application in accordance with some embodiments ofthe present invention;

FIG. 4 depicts an illustrative install screen of the softwareapplication in accordance with some embodiments of the presentinvention;

FIG. 5 depicts an illustrative garage screen of the software applicationin accordance with some embodiments of the present invention;

FIG. 6 depicts an illustrative garage screen of the software applicationshowing some vehicles unlocked in accordance with some embodiments ofthe present invention;

FIG. 7 depicts another illustrative garage screen of the softwareapplication showing some vehicles unlocked in accordance with someembodiments of the present invention;

FIG. 8 depicts another illustrative garage screen of the softwareapplication in accordance with some embodiments of the presentinvention;

FIG. 9 depicts yet another illustrative garage screen of the softwareapplication showing some vehicles unlocked in accordance with someembodiments of the present invention;

FIG. 10 depicts yet another illustrative garage screen of the softwareapplication in accordance with some embodiments of the presentinvention;

FIG. 11 depicts an illustrative car card of an locked vehicle afterselecting the corresponding commands in accordance with some embodimentsof the present invention;

FIG. 12 depicts another illustrative car card of another locked vehicleafter selecting the corresponding commands in accordance with someembodiments of the present invention;

FIG. 13 depicts yet another illustrative car card of yet another lockedvehicle after selecting the corresponding commands in accordance withsome embodiments of the present invention;

FIG. 14 depicts an illustrative screen showing that the softwareapplication did not receive a still image and instructs the user to tryagain in accordance with some embodiments of the present invention;

FIG. 15 depicts an illustrative screen showing that the softwareapplication successfully receives a still image in accordance with someembodiments of the present invention;

FIG. 16 depicts one of the additional steps that the user may need totake in order to unlock a vehicle in accordance with some embodiments ofthe present invention;

FIG. 17 depicts another one of the additional steps that the user mayneed to take in order to unlock a vehicle in accordance with someembodiments of the present invention;

FIG. 18 depicts yet another one of the additional steps that the usermay need to take in order to unlock a vehicle in accordance with someembodiments of the present invention;

FIG. 19 depicts yet another one of the additional steps that the usermay need to take in order to unlock a vehicle in accordance with someembodiments of the present invention;

FIG. 20 depicts yet another one of the additional steps that the usermay need to take in order to unlock a vehicle in accordance with someembodiments of the present invention;

FIG. 21 depicts that a vehicle has been unlocked and the lock statusindicator of the command of the vehicle has been changed from one colorto another color in accordance with some embodiments of the presentinvention;

FIG. 22A depicts a detailed view of an illustrative machine learningsystem in accordance with some embodiments of the present invention;

FIG. 22B depicts a simplified view of the machine learning system ofFIG. 22A in accordance with some embodiments of the present invention;

FIG. 23 depicts an illustrative 3D matrix of pixel values of an image inaccordance with some embodiments of the present invention;

FIG. 24A depicts an illustrative feature learning component transferringprocess in accordance with some embodiments of the present invention;

FIG. 24B depicts an illustrative image classification component trainingprocess in accordance with some embodiments of the present invention;and

FIG. 24C depicts an illustrative machine learning system based on theprocesses in FIGS. 24A-24B in accordance with some embodiments of thepresent invention.

DETAILED DESCRIPTION OF THE INVENTION

Embodiments of the present invention are related to a softwareapplication configured to identify vehicles (e.g., cars) that can beused to obtain information about the vehicles. The software applicationcan be installed on a computing device that includes a camera configuredto capture still images. After the software application is installed,the user can activate the camera through the software application andpoint the camera at a vehicle. The software application includes anobject recognition function that can recognize the vehicle in the viewof the camera. After recognizing the vehicle, the software applicationdisplays information about vehicle on a screen of the computing device.The information can include the manufacturer of the vehicle (e.g.,Toyota), the model number or name of the vehicle (e.g., Camry), the yearin which the vehicle is made or to be debuted (e.g., 2025), and otherinformation (e.g., horse power, engine size, torque, chassis dimensions,etc.) about the vehicle.

In one embodiment, the cars to be identified can be presented in aconfined environment, such as an auto show. In addition to specificallyidentifying the cars, the identified information can be used for otherpurposes. For example, a number of different vehicles can be identifiedas part of a contest, e.g., to see how many different cars can beidentified by a particular user. Additionally, the identification ofvehicles can be used as part of a scavenger hunt or other contest. Ifdesired, awards can be given for successful participation in the contestor for those who complete the contest more quickly or effectively thanthe other participants.

Every time the user uses the camera to identify a vehicle, theidentified vehicle is “unlocked” by the software application and savedin a “garage” (database) of the software application. This allows theuser to collect information on a number of different vehicles. Thegarage stores all the vehicles the user has unlocked and the user canaccess the unlocked vehicles stored in the garage and their informationat a later time. The software application can also track the number ofvehicles the user has unlocked and the number of vehicles remain to beunlocked. For example, the software application can be implemented as ascavenger hunt game which requires the user to unlock all the vehiclesin the auto show. After unlocking all the vehicles, the user can beawarded a prize. The first user to unlock all vehicles can be given anadditional award. An award can be, for example, a monetary award ornon-monetary award (e.g., coupons, discounts, gift cards, free magazinesubscription, tangible items, services, etc.). The user can also begiven prizes in stages, such as unlocking X number of vehicles entitlesthe user to receive prize A, unlocking Y number of vehicles entitles theuser to receive prize B, and unlocking all the vehicles entitles theuser to receive the top prize. The software application encourages theuser to visit all the vehicles introduced at the show and travel todifferent locations in the venue in which the vehicles are shown.

FIG. 1 depicts part of the user interface of the software application100. FIGS. 2-3 depict illustrative operations of (or user interactionwith) the software application 100 that are understood from thediscussion of FIG. 1 below. FIGS. 4-21 depict illustrative screen shotsof the software application. The discussion below about the userinterface primarily relies on FIG. 1 and will refer to other figuresfrom time to time for better understanding the present invention.

After the software application is selected by the user (e.g., bypressing an icon of the software application displayed on the screen ofthe handheld mobile device), the computing device loads the softwareapplication and the software application presents a splash or landingscreen 105 (main or first screen 105) including one or more commands foruser selection. FIG. 4 depicts an illustrative first screen 105. Thecommands, for example, include a garage command 115, a camera command120, and a settings command 125. The user can select the garage command115 to view all the vehicles to be unlocked by the user. Selection ofthe garage command 115 causes the software application to display screen110 in which all the vehicles to be unlocked are shown. FIGS. 5, 8, and10 depict an illustrative garage screen 110. FIGS. 6, 7, and 9 depict anillustrative garage screen 110 showing some vehicles unlocked. Screen110 includes a command 150 for each vehicle to be unlocked andconfigured for selecting that particular vehicle. The command 150 mayinclude a status indicator showing whether the vehicle (the vehicleassociated with the command) is locked or unlocked. For example, theindicator may be presented in the form of color (e.g., with the commandshown in black and white to indicate that the vehicle is locked and withthe command shown in color to indicate that the vehicle is unlocked) ortext (having the command displaying either “locked” or “unlocked”). Forexample, referring to FIG. 10, the square box associated with Acura RDXis a command 150. That command is shown in black and white because thatvehicle has not been unlocked.

Selection of the command 150 causes the software application to displaya screen such as screen 127 or a “car card” 127 showing informationabout the selected vehicle. When the user selects a vehicle that has yetto be unlocked (locked vehicle), the selection causes the softwareapplication to display car card 127 showing vehicle identificationinformation and lock status indicator of the selected vehicle (e.g.,vehicle A). Vehicle identification information may contain only the nameof the manufacturer (e.g., Infiniti) and the model of the vehicle (e.g.,Q60). The lock status indicator may have the image of the selectedvehicle (e.g., an image pre-stored in the database) displayed in blackand white. The vehicle identification information, lock statusindicator, and image of the vehicle may be the information on car card127. In this example, vehicle A is assumed to be a locked vehicle. FIGS.11-13 depict illustrative car cards 127 of locked vehicles afterselecting their corresponding commands 150.

When the user selects a vehicle that has been unlocked (unlockedvehicle), the selection causes the software application to display afirst car screen 130 or car card 130 showing vehicle identificationinformation and lock status indicator of the selected vehicle (e.g.,vehicle B). Vehicle identification information may contain only the nameof the manufacturer (e.g., Jeep) and the model of the vehicle (e.g.,Wrangler). The lock status indicator may have the image of the selectedvehicle displayed in color. Car card 130 of an unlocked vehicle isprovided with a command 131 configured to access a second car screen 133or content card 133. Content card 133 includes additional informationabout the selected vehicle. Additional information may include adescription of the selected vehicle (e.g., best SUV of the year,suitable for off-road terrains, etc.) and technical and otherinformation of the selected vehicle (e.g., horse power, torque, timerequired from 0 to 60 MPH, starting price, etc.). Content card 133 isprovided with a command 134 configured to return the garage screen 110to car card 130. Car card of a locked vehicle such as 127 is configuredwithout such a command 131. The software application may includeadditional car cards for other vehicles shown in garage screen 110(e.g., car card 135 for vehicle C).

The vehicles to be unlocked and the car cards and content cards of thosevehicles (and the information on those cards) are stored in a databaseof the software application and can be accessed and displayed by thesoftware application on the garage screen. Car card and content card maycontain information about the vehicle such as an image of the vehicle(e.g., an image pre-stored in the database), the manufacturer of thevehicle (e.g., Ford), the model number or name of the vehicle (e.g.,Mustang), the year in which the vehicle is made or to be debuted (e.g.,2022), and other information (e.g., horsepower, engine size, torque,chassis dimensions, time required from o to 60 MPH, starting price,etc.) about the vehicle. For example, a car card may contain an image ofthe vehicle, the manufacturer of the vehicle, the model number or nameof the vehicle, the year in which the vehicle is made or to be debuted,whereas a content card may contain other information such as horsepower,engine size, torque, chassis dimensions, time required from 0 to 60 MPH,starting price, etc.

The database in general is inaccessible to the user (or the user haslimited access) and is accessible only by the software application. Theuser can access the database or the cards in the databse only when heunlocks a vehicle. Before the user unlocks a vehicle, the user haslimited access to the database such as the car cards or commands shownin FIGS. 11-13 or the information on those cards or commands. After theuser unlocks the vehicle, the software application accesses the databaseand retrieves the corresponding cards (e.g., car card and content card).Car card and content card of the unlocked vehicle are made available tothe user and the user can view the cards or information in the cardsimmediately after he unlocks the vehicle and at a later time (e.g., whenthe he accesses or logs into the software application the next time).Cards of the other vehicles (locked vehicles) remain inaccessible to theuser until they are unlocked by the user. Information about whichvehicles have been unlocked and are still locked are tracked and storedby the software application. This information may be used by thesoftware application to determine the appropriate prizes based on theuser's vehicle unlocking progress or his final unlock result.

The software application is configured without an option allowing theuser to directly access the database, e.g., without a database commandthat permits the user to view all the car cards and content cardsdirectly. The software application may be configured to have a developeroption that allows developers of the software application to access thedatabase. In some embodiments, such option or access can be authorizedby the developers to the users in some situations, such as the user andthe developer, or the entity for which the software application is madefor, have subscription service and the developer, or entity, may allowthe user to pre-unlock one or more vehicles before or during thecontest. The database or the cards in the database are accessiblethrough the garage screen 110 and the user may not be able to avoid orskip the garage screen 110 in order to reach the database. The garagescreen serves as a control interface or gatekeeper between the user andthe database. The cards are available only through commands 150 in thegarage screen 110. For example, one of the commands 150 provides accessto car card 127 only when vehicle A is locked (not content card ofvehicle A) and another one of the commands 150 provides access to bothcar card 130 and content card 131 after vehicle B is unlocked.

FIG. 1 shows that three (3) car cards in the database and the user canselect one of the cards for viewing vehicle information (e.g., vehicleidentification information, lock status, and image of the selectedvehicle) about that vehicle from the garage screen 110. For example, theuser may select vehicle B from the garage screen 110, and the softwareapplication then retrieves car card 130 of vehicle B from the databaseand displays that card on the garage screen 110.

The garage screen 110 is updated according to the user's vehicleunlocking progress. Before any of the vehicles is unlocked, commands 150on the garage screen 110 have a status indicator showing that they arelocked (e.g., the vehicle images in commands 150 are shown in black andwhite). When a vehicle is unlocked, the status indicator of that vehicleor command is changed to show that it has been unlocked (e.g., thevehicle image in the corresponding command is changed to color).

The car card and content card, in operation, may be shown as one card onthe garage screen 110. The car card may be shown as the front of thecard whereas the content card may be shown as the back of the card. Thesoftware application displays the car card first when the command 150 isselected, and then the content card when the command 131 is selected (ifthe vehicle is unlocked). The software application may show the contentcard by turning over the card (the car card), revealing the back side ofthe car card, to make the cards appearing as one card.

The camera command 120 (FIG. 1) is configured to turn on the camera ofthe computing device. Upon selecting the camera command 120, thesoftware application displays the view of the camera or a capturingscreen 140 on the display of the computing device. The user can directthe view or capturing screen 140 to a vehicle in the auto show and thesoftware application, through the object recognition function, canidentify the vehicle in the view. The software application through theobject recognition function can recognize the vehicle and display therelevant information in real-time or before the user presses the imagecapturing button (e.g., as the vehicle appears in the viewfinder or onthe screen of the computing device such as by the software applicationdisplaying text or other indicator over the view of the view screen,splashes an icon or text when the view screen is showing a vehicle andthe software application automatically recognizes that vehicle). Theobject recognition function can also recognize the vehicle and displaythe relevant information after the user presses the image capturingbutton (after an image is captured). The user can point the camera atthe vehicle in different angles and the object recognition function canrecognize the vehicle in different angles. The object recognitionfunction can recognize the vehicle when the entire vehicle or a portionof the vehicle appears in the view of the camera.

After the vehicle is identified, the vehicle is unlocked by the softwareapplication. “Unlock” means that both the car card and content card ofthe vehicle, or information on those cards, are available to the userthrough the garage screen 110, or through a first car screen (e.g., 130)and a second car screen (e.g., 133) displayed on the garage screen 110.The user can select the command 150 of the unlocked car to access thecar card and content card. Before the vehicle is identified, the vehicleis locked by the software application. “Lock” means that both the carcard and content card of the vehicle, or information on those cards, areunavailable to the user through the garage screen 110 or other parts ofthe software application. “Lock” may also mean that only the car card,or only information from that card, is available to the user through thegarage screen 110. When the user selects the command 150 of the lockedvehicle, the software application may indicate to the user that thevehicle is locked and disallow access to the car card and content card,or indicate to the user that the vehicle is locked and provide accessonly to the car card and restricts access to the content card.

In response to identifying a vehicle, the software application maycommunicate with and cause the network interface of the mobile device tocommunicate with a database, a server, or other computer system over acommunications network (e.g., internet). The server may be configured tostore the user's unlocking progress that is accessible through adifferent computing device. As such, the user can access his progressand obtain vehicle information of the unlocked vehicles from a differentdevice. The server may also be configured to store multiple user'sunlocking progress, track their progresses, determine the first personto unlock all the target vehicles in a contest or auto show, andcommunicate to other users/mobile devices when a person unlocks all thetarget vehicles in the contest or auto show. The server may also beconfigured to communicate to other users/mobile devices when a person isclose to unlocking all the target vehicles (e.g., someone has unlocked16 out of 17 target vehicles) or is far ahead than other participants(e.g., someone has unlocked 50% of the target vehicles). The server mayalso notify the host of the contest (or a computer of the host) or athird party prize provider (or a computer of the third party prizeprovider) when a winner has emerged. The server may be configured tocommunicate aforementioned information to participants (or their mobiledevices), hosts (or their computer systems), and third party prizeproviders (or their computer systems) when a user identifies a vehicleusing the software application (e.g., the server communicates thatsomeone has unlocked all the target vehicles only when someone hasunlocked all the target vehicles using the software application). Theserver or database may also be configured to communicate otherinformation.

A database storing prize information may be implemented in the softwareapplication, or on a different software application or computing devicethat the software application can communicate with over a communicationsnetwork to obtain prize information. The prize information database, bydefault, may be configured to be locked by the software application.Accessibility to the database will require the user to use the softwareapplication to identify target vehicles and identify a certain vehicleor a certain number of vehicles. The software application tracks theuser's progress and unlocks the corresponding prize based on the user'sprogress. When the user reaches a certain progress, the database isunlocked and the software application can access thecorresponding/unlocked prize information based on the user's progress.The other prizes remain locked and inaccessible by the softwareapplication. The database may store coupon codes, gift card numbers,links to discounted products or services that cannot be otherwiseobtained, or other prizes. The software application presents the couponcodes, gift card numbers, and/or links to the user through the screen ofthe mobile device when the user reaches a certain progress. In someembodiments, upon the user reaches a certain progress, the softwareapplication can automatically initiate a web browser or other tools orresources on the mobile device and take the user to where the prizes are(e.g., because the prizes are stored on a different location, softwareapplication, computing device). The server mentioned above maycommunicate with the prize database to provide the applicable prizeinformation to the corresponding user or based on that user's and/orother users' unlocking progress.

The object recognition function can recognize vehicles beyond thosepresented at the auto show (e.g., the user can point the camera at arandom car on the street and the software application can recognize thatcar), but vehicles recognized outside the auto show are not “unlocked”by the software application, or are not saved and tracked by thesoftware application for determining rewards (unless the vehicles at theauto show are publicly available). The database includes vehiclesidentifiable by the object recognition function. The identifiablevehicles include vehicles beyond those presented at the auto show and agroup of target vehicles that can be found at the auto show. The targetvehicles are vehicles that can be unlocked by the software application.In some embodiments, the database may contain only the target vehiclesand the object recognition function may be configured to recognize onlythe target vehicles. Since vehicles introduced in an auto show aregenerally unique, future, or concept vehicles, the target vehicles maybe available only at the auto show. The software application isprimarily configured to operate with the auto show event. In addition torecognizing actual vehicles, in some embodiments, the softwareapplication may also recognize photographs or printouts containing suchvehicles.

When the user captures a still image of a vehicle in the auto show andthe object recognition function recognizes that the vehicle in the image(captured by the camera) is a target vehicle, the software applicationcan communicate to the user that he has captured a target vehicle andrequires the user to take additional steps in order to unlock thevehicle. For example, the software application may bring the user to thegarage screen, instruct the user to select the command (150) of thevehicle, provide a command requiring the user to press and hold thatcommand for a period of time. After the extra steps, the user then hasaccess to the car card and content card of the target vehicle. FIGS.16-20 depict these steps and relevant screen shots and cards. FIG. 21shows that the vehicle (e.g., Jeep Wrangler) has been unlocked and thelock status indicator of the command (150) of that vehicle has beenchanged from black and white to color. Requiring the user to press andhold the command (FIG. 17) for a period of time may be an operation usedby the software application to save and track the user's vehicleunlocking progress in the user's account. The software application mayalso communicate prize information to the user if applicable, such aswhen the user unlocks a particular vehicle or unlocks enough vehicles tobe eligible for the prize. Prize information may include the prize theuser receives for unlocking a particular vehicle or a certain numbervehicles.

FIG. 14 depicts an illustrative screen showing that the softwareapplication did not receive a still image and instructs the user to tryagain. FIG. 15 depicts an illustrative screen showing that the softwareapplication successfully receives a still image.

The settings command 125 (FIG. 1) is configured to present a settingsscreen 145 after selection. The settings screen 145 may include one ormore commands configured to view information about the auto show (e.g.,the name of the auto show, the name of the entity that hosts the autoshow, the name of the sponsors, the names of the automobile manufacturesparticipating in the auto show, the total number of cars beingdisplayed, etc.) and to make changes related to the software application(e.g., changing the theme, format, style, or font of the softwareapplication) upon selection.

In some embodiments, the screen 105 may be the initial screen of thesoftware application that appears on the display of the computing deviceafter the computing device finishes loading the software application.The initial screen may include the garage command, camera command, andsettings command described above. The garage screen 110 may appear inresponse to the user selecting the garage command in the screen 105. Thecamera command and settings command may also be available for selectionfrom the garage screen 110 after the garage command on the screen 105 isselected. The camera view or capture screen 140 may appear on thedisplay of the computing device after the user selects the cameracommand from the initial screen 105. The car card 127, 130, or 135 mayappear on the display of the computing device after the user selects avehicle from the garage screen 110. The settings screen 145 may appearon the display of the computing device after the user selects thesettings command from the initial screen 105.

The software application also includes other commands, such as tutorialcommands configured to display screens explaining the operation of thesoftware application and the functionalities of the garage and cameracommands upon selection, reward commands configured to display screensshowing rewards that the user has received based on the user's vehicleunlocking progress. FIGS. 4 and 5 depicts some of these commands. Thesoftware application also allows the user to create an account that canbe used to save and track the user's vehicle unlocking progress orresult.

A command allows the user to interact with the software application.Each user interface includes one or more commands. A command permits theuser to select an area in the user interface or screen of the computingdevice (e.g., by pressing his finger on the area or screen) to instructthe software application to further display additional information orperform additional functions. For example, a command can be a button,window, icon, button, and the like. A command can also be selectedthrough voice, audio, gestures, or other manners.

The object recognition function is implemented on a machine learningsystem. In one embodiment, as shown in FIG. 22A, the machine learningsystem 2000 includes a first artificial neural network (feature learningcomponent) 2005 and a second artificial neural network (imageclassification component) 2010. The first neural network 2005 isconfigured to receive a lower resolution image of the image or framecaptured by the camera, detect features in the lower resolution image(e.g., patterns, edges, shapes, colors, and other characteristics thatare used to delineate an object in the image), and compute a vectorrepresenting the detected features. The image is captured by the camerain original resolution or a first resolution. In some embodiments, theoriginal resolution image may also be used without being processed toreduce its resolution (use as-is).

The first neural network 2005 is configured to receive an image,regardless of its resolution, composed of matrices of pixel values. FIG.23 depicts illustrative matrices of pixel values of an image. Eachmatrix 2020 includes a width and height and each of the width and heightincludes a number of pixels. Each pixel is represented by a numericalvalue 2015 that is between 0 and 255. The pixel value 2015 representsthe amount of color, e.g., the amount of redness, greenness, orblueness. There is a matrix for each color channel that produces theimage, In FIG. 23, there are three color channels and there is matrixfor each red, green, and blue color channel. The number of colorchannels determine the depth of the matrices. The matrices in FIG. 23 isa three-dimensional (3D) matrix (width, height, and depth. Although thefigure only shows that the measurements of the width and height are 4pixels each and that the image is produced by three colors, other widthand height measurements and numbers of color channels are contemplated.For example, the image captured by the camera may be produced using fourcolors and has a resolution of 1,920 pixels×1,080 pixels. The fourcolors may be cyan, magenta, yellow, and black and there is atwo-dimensional matrix for each color (e.g., a 1,920×1,080 matrix forcyan, a 1,920×1,080 matrix for magenta, a 1,920×1,080 matrix for yellow,and a 1,920×1,080 matrix for black). The 2D matrix of all the colorstogether form a 3D matrix. Therefore, the 3D matrix has a dimension of1920×1,080×4.

In one embodiment, the resolution or dimension of the captured image isreduced to a lower resolution or dimension (e.g., by down-sampling orcropping). For example, the image may be reduced to 224×224×4 whichincludes a 224×224 matrix in cyan, a 224×224 matrix in magenta, a224×224 matrix in yellow, and a 224×224 matrix in black. The resultingimage may be referred to as the lower resolution image. The first neuralnetwork then computes a features vector using the lower solution image.The features vector is obtained by further reducing the resolution ordimension of the lower resolution image. For example, the first neuralnetwork may compute a features vector with a dimension of 1×1,024 fromthe 224×224×4 dimension. Instead of having four different colors or datato describe a feature in the image, the first neural network performs aconvolution process on the lower resolution 3D image to convert it intoone-dimension (1D) data representative of the 3D image or its features.The 1D data includes multiple blocks of data where each block includesinformation conveying the four different colors or data describing thefeature in one single block of data, as opposed to four data blocks orvalues of the four colors. The 1D data is the features vector. In thedimension of 1×1,024, there are 1,024 data blocks.

Other resolutions, dimensions, and number of color channels are alsocontemplated. The software application is configured to reduce theresolution of the image captured by the camera (e.g., 1,920 pixels×1,080pixels×4 color channels) and produce an image of the captured image orframe with reduced resolution (e.g., 224 pixels×224 pixels×4 colorchannels).

In one embodiment, referring, back to FIG. 22A, the first neural network2005 is implemented using a convolution layer 2006, a rectified linearunit (ReLu) layer 2007, and a pooling layer 2008. The first neuralnetwork 2005 may include multiple convolution layers, ReLu layers, andpooling layers to compute the features vector. The convolution layer,ReLu layer, and pooling layer perform multiple iterations (e.g., on ascale of thousands or hundreds of thousands) to determine the finalfeatures vector.

The output of the first neural network 2005 is connected to the input ofthe second neural network (image classification component) 2010. Thesecond neural network 2010 includes category labels implemented on asoftmax layer 2012 and is configured to classify the features vector toone of the labels using a fully connected layer 2011. The second neuralnetwork 2010 classifies the features vector to the appropriate categorylabel by determining the likelihood or percentage that the featuresvector falls into each category label (e.g., a classification score) andassigning the features vector to the category label with the highestpercentage (e.g., 1% of chance that the features vector falls intocategory label A, 6% of chance that the features vector falls intocategory label B, and 93% of chance that the features vector falls intocategory label C, and assigning the features vector to category labelC).

In the process of determining the percentage or classification score,the second neural network produces a 1-dimensional (1D) output vectorusing the features vector. The resulting 1D output vector has a vectorlength that is equal to the number of category labels.

By classifying or assigning the features vector to the proper categorylabel, the image for which the features vector is computed (the imagecaptured by the camera) is also classified to a category label. As such,the machine learning system can determine that, for example, the vehiclein the camera view (e.g., an image captured by the camera) is a Ford GT(e.g., Ford GT is one of the category labels). The category labels, forexample, may include a target vehicle label for each target vehicle inthe auto show (e.g., if there 17 target vehicles, then there are 17target vehicle labels), a non-target vehicle label for non-targetvehicles or vehicles outside the auto show, and a non-vehicle label forobjects or scenes other than cars. For instance, the second neuralnetwork may be implemented with 19 category labels that contain 17target vehicle labels, a non-target vehicle label, and a non-car label(boats, airlines, etc.). Each displayed vehicle in the auto show may bea target vehicle. Target vehicles may also be only a certain vehicles inthe auto show.

FIG. 22A is a low-level or detailed view of the machine learning system2000 whereas FIG. 22B is a high-level or simplified view of the machinelearning system 2000.

The first neural network can be trained and obtained from another orlarger neural network that is suitable for mobile and embedded basedvision applications. Such a larger or suitable neural network 2050 isshown in FIG. 24A. The neural network 2050 includes a dataset of images2055, an image feature learning component 2060, and an imageclassification component 2065. The neural network 2050 includes a largedataset of images, e.g., more than 1 million of different imagescovering a wide range of things such as dogs, cats, cars, boats,airplanes, buildings, etc. The feature learning component 2060 isimplemented using a convolution layer, a ReLu layer, and a pooling layer(or multiple such layers). The image classification component 2065includes category labels implemented on a softmax layer (e.g., a doglabel, a cat label, a car label, etc.). The feature learning component2060 is configured to detect features (e.g., patterns, edges, shapes,colors, and other characteristics that are used to delineate an objectin the image) in an image from the dataset 2055 and produce a result(e.g., an image features vector) representing the detected features. Theresult is provided to the image classification component 2065 thatclassifies the result to one of the labels using a fully connected layer(e.g., the result corresponds to a dog label and therefore the imagefrom the dataset includes a dog). The images from the dataset 2055 arefed to the feature learning component 2060 and the convolution layer,ReLu layer, and pooling layer perform multiple iterations (e.g., on ascale of thousands or hundreds of thousands) to compute an imagefeatures vector representing the detected features. The result of eachimage is provided to the image classification component 2065 thatclassifies each result or image to one of the labels using a fullyconnected layer. This larger neural network 2050 may be referred as ageneral task neural network in that it can identify all types of objectsin the image such as dogs, cats, cars, boats, airplanes, buildings,etc., as opposed to being configured to identify only a specific type ofobject such as cars.

The feature learning component 2060 is then separated from the generaltask neural network 2050 and used as the first neural network in themachine learning system described in this application. The featurelearning component 2060 is also used in a custom neural network 2070. Acustom neural network is a neural network that can be configured toperform a specific task. For example, a custom neural network may beconfigured to be a domain-specific neural network. The domain-specificneural network may identify only a specific type of object or may betrained to identify only a specific type of object. For example, thespecific type of object may be cars or cars that do not appear in theimage dataset of the general task neural network (e.g., target vehiclesor vehicles at the auto show for which the dataset does not haveimages). The custom neural network 2070 includes a dataset of images2075, an image feature learning component 2080, and an imageclassification component 2085. The dataset of images 2075 includesimages of different angles and different portions of the object ortarget vehicles. The feature learning component 2060 from the generaltask neural network 2050 is transferred to the custom neural network2070 and is used as the image feature learning component of the customneural network 2070. The image classification component 2085 isimplemented using a fully connected layer 2086 and a softmax layer 2087in their original, default, or untrained form.

In such a form, the fully connected layer 2086 is configured to be readyfor producing a features vector having a vector length that matches thenumber of category labels created on the softmax layer 2087 (e.g., thelayer can produce a features vector having a matching vector lengthafter the images from the dataset 2075 are supplied to the custom neuralnetwork 2070). In some embodiments, the fully connected layer 2086 mayhave a default vector that has a default vector length and be configuredto adjust the default vector length to match the number of categorylabels created on the softmax layer 2087 after the images from thedataset 2075 are supplied to the custom neural network 2070.

The softmax layer 2087 is configured to be ready for producing a numberof category labels (e.g., the layer can produce a number of categorylabels after the images from the dataset 2075 are supplied to the customneural network 2070). The softmax layer 2087 may also have a number ofcategory labels pre-created, but each label is not associated with aspecific type of object (“empty category labels”). The softmax layer2087 can create the appropriate category labels and number of categorylabels after the images from the dataset 2075 are supplied to the customneural network 2070.

The image classification component 2085 of the custom neural network2070 is then trained or modified using the images 2075 for the customneural network 2075, or images containing objects that do not appear inthe images 2055 used in the general task neural network 2050. Forexample, the images used in the general neural network may include animage of a Ford Taurus and an image of Ford Focus but does not includean image of a Ford GT or the Ford GT displayed on the auto show. Imagessuch as the one of the Ford GT and other target vehicles displayed onthe auto show are used to train or modify the fully connected layer 2086and the softmax layer 2087. For example, there may be 17 target vehiclesand the images may include 10 images of each target vehicle (taken atdifferent angles or different portions of the vehicle). All the images2075 are provided to the input of the custom neural network 2070, andthe fully connected layer 2086 and the softmax layer 2087 then determinea features vector with a vector length that matches the number ofcategory labels and create the appropriate category labels (e.g., 17labels since there 17 target vehicles, such as a Ford GT label, a 2025Toyota Camry label, a Honda Concept SUV, etc.), respectively. Based onthe provided images or other additional images, the softmax layer canalso create a non-target vehicle category label and a non-car label inaddition to the 17 target vehicle category labels, with a total 19category labels. Additional vehicle category labels can be created bysupplying images of additional target vehicles.

The feature learning component 2080 and the trained image classificationcomponent 2090 are then used as the machine learning system 2095, whichis the machine learning system 2000 shown in FIGS. 22A and 22B.

The machine learning system described in this application solvesproblems associated with conventional machine learning systems. First,conventional machine learning systems are computationally intensive.Conventional machine learning systems use traditional convolutiontechniques that require processing a large number of parameters. Thelayers in the neural network include many nodes, labels, weights, andother parameters connected together. The connected nodes, labels,weights, and other parameters are known as a graph or graphs.Traditional convolution techniques methods need to analyze every oralmost every parameter in the graph and perform a series of mathematicaloperations (e.g., addition, subtraction, multiplication, and division).These analysis and operations, or floating point multiplicationoperations, demand substantial computing power. The machine learningsystem described in this application uses the feature learning componentfrom a neural network that is suitable for mobile and embedded basedvision applications. The feature learning component from such a neuralnetwork uses depthwise separable convolutions, as opposed to traditionalconvolutions, that reduce the number of parameters to be analyzed andthe number of floating point multiplication operations and employbitwise operations. In depthwise separable convolutions, some of thenodes, labels, weights, and other parameters in the graph may bediscarded and the other nodes, labels, weights, and other parameters maybe simplified (e.g., into 1 s and 0 s) by the convolutions. Thesimplified nodes, labels, weights, and other parameters are thenprocessed using bitwise operators (e.g., AND, OR, XOR gate, etc.). Theparameter reduction and bitwise operations are known as quantization.Depthwise separable convolutions include depthwise convolution followedby pointwise convolution. As such, the described machine learning systemcan identify an object or target vehicle faster than existing machinelearning systems and by using less computational power or battery, whichis critical and limited on mobile devices. As discussed earlier. thesoftware application is installed on computing devices and computingdevices are preferably mobile devices.

Second, conventional machine learning systems are configured to performgeneral tasks (e.g., identifying whether the object in the image is acat, dog, car, boat, airplane, etc.) and may not be accurate inperforming specific tasks (e.g., identifying whether the object in theimage is a particular type of car, such as a Toyota Camry, Honda Accord,Ford Taurus, etc.). Training such systems are also complicated and timeconsuming because they require using a very large set of images andtraining the entire system, e.g., the convolution, ReLu, and poolinglayers in the feature learning component and the fully connected andsoftmax layers in the image classification component. In the describedmachine learning system, the entire system does not need to be trainedfrom scratch. The described machine learning system employs the featurelearning component from a general task machine learning system that hasalready been trained. Therefore, in training the described machinelearning system, the feature learning component only needs to bepartially trained for the specific task and only the fully connected andsoftmax layers need to be trained from scratch. The described machinelearning system also is also trained using a much smaller set of images(for the specific task) compared to a general task machine learningsystem. Therefore, the described machine learning system can be builtwith less training time and produce higher accuracy for task-specificjobs.

In another embodiment, the machine learning system is based on apre-trained neural network (first neural network) and a neural network(second neural network) to be trained using the pre-trained neuralnetwork or knowledge gained from the pre-trained neural network. Thepre-trained neural network can detect patterns, edges, shapes, colors,and other characteristics in an image or frame (the image featuredetection component or feature learning component), and use the detectedpatterns, edges, shapes, and other characteristics to determine what theunderlying object in an image is (the image determination component orimage classification component).

The first neural network is pre-trained using pattern detectionprocesses, shape detection processes, color detection processes, andother processes. After the first neural network is trained to detectpatterns, edges, shapes, and other characteristics, the first neuralnetwork is further trained to determine what the object in an image isusing those detection capabilities by providing the first neural networkwith a number of images containing different objects (e.g., cats, dogs,cars, boats, airplanes, etc.). The images can depict different types ofthe object and be classified according to those types. The first orpre-trained neural network takes a 224×224 pixel 4-channel image as aninput and computes a 1×1024 vector representation of the image'sfeatures. After training the first neural network with those images, thefirst neural network can determine that the object in a new image(received after the training session or in a non-training operation) isobject A.

The machine learning system includes the detection component (orlearning component), or knowledge gained from the detection component,and combines the detection component with the second neural network. Thesecond neural network is configured to receive a set of images that aredifferent from the set received by the first neural network. The imagesets may be different in that both sets cover the same or similar typesof objects (e.g., cats, airplanes, and boats), but the image set used inthe second neural network (the second set) includes additional imagesfor the same or similar types of objects that are not in the set used inthe first neural network (the first set). For example, the second setmay include images of the same cats, airplanes, or boats in the firstset but are taken at different angles or show different portions of thesame cats, airplanes, or boats. The image sets may also be different inthat the first set includes a number types of objects (e.g., cats,airplanes, and boats) and the second set includes images of another typeof object (e.g., cars) that is not in the first set. The image sets mayalso be different in that the first set covers several types of objects(e.g., cats, airplanes, boats, and cars) and the second set includesimages to supplement a particular type of object (e.g., cars that are noin the first set). For example, the set of images received by the secondneural network includes an image of Ford FT that is not in the setreceived by the first neural network. Other combinations and variationsare also contemplated.

The number of images received by the second neural network (e.g., on thescale of 100 or 1000) is also much smaller than the number of imagesreceived by the first neural network (e.g., on the scale millions). Thesecond neural network is trained to determine what the objects are inthe smaller set by using the detection capabilities from the detectioncomponent by providing the second neural network with the smaller set ofimages. After training the second neural network with those images, thesecond neural network can determine that the object in a new image(received after the training session or in a non-training operation) isobject B. The second neural network includes a fully connected layer anda softmax layer. The second neural network takes as an input, the 1×1024representation of the image, and produces a 1D output vector with thesame length as the number of categories.

Weights learned from training deep networks on large but generic datasets (pre-trained neural network) can be effectively used to initializemodels for other domains and other data sets. The image features(patterns, edges, object etc.) learned by the base model (first orpre-trained neural network) generalize well to the more specificclassification task performed by the top layer (second neural network).Conversely, the top layer can be effectively trained with much less datagiven that it does not have to learn low-level image features fromscratch.

The machine learning system including the detection component of thefirst neural network and the second neural network is provided with adataset containing vehicle images (e.g., 250,00 images or other numberof images) from a number of different sources. The images can beclassified into different vehicle categories by make, model, year, andother factors. These categories can be further combined or used in amanner to provide a number of higher level categories (e.g., Honda CRV,unknown SUV) that can be used by the software application to easilycommunicate the identified vehicle to the user. The machine learningsystem is trained with those images by using detection capabilities fromthe detection component in a private Cloud environment or other privateenvironment. After training the second neural network, this neuralnetwork becomes a trained neural network for vehicle images.

In one embodiment, the machine learning system includes a characteristicdetection system configured to detect characteristics (e.g., patterns,edges, shapes, etc.) in a dataset (e.g., a set of images) regarding anobject (e.g., each image contains the object) and a second systemtrained using the characteristic detection system and a dataset (e.g.,another set of images) regarding another object (e.g., each image in theother set contains another object or different object). Thecharacteristic detection system is trained to make such detections byusing a dataset regarding an object. For instance, the system isprovided with a number of images that each contains the object. Thesystem is trained to detect patterns, edges, and/or shapes in thoseimages. The characteristic detection system may be a pre-trained neuralnetwork or a component of a pre-trained neural network. The secondsystem is trained using the characteristic detection system to recognizeor identify the other object in each image of the other set. Forinstance, the system is provided with a number of images that eachcontains a different object. The system is trained to recognize oridentify the other object in the other set of images by using thepattern, edge, and/or shapes detection techniques learned from thecharacteristic detection system.

For example, when the user at the auto show captures an image using thesoftware application, the image's data is sent to the machine learningsystem. After the machine learning system analyses the data, the systemprovides information about the vehicle and the information is displayedon the screen of the computing device. The machine learning system mayalso provide information about possible vehicles that match the vehiclein the image and the confidence score for each possible vehicle.

Preferably, the machine learning system or the software applicationcontaining the machine learning system is configured to operate locallyon the computing device. The machine learning system or the softwareapplication is installed on the computing device and operates on thecomputing device without communicating with another computer system orserver over a network. This implementation allows the machine learningsystem to operate without depending on a network connection and willwork in the absence of an Internet connection. On-device operationallows provides a faster user experience because the machine learningsystem does not need to communicate with the Cloud or other devices overa network. Users will be able to receive real-time identification resultfrom the machine learning system in the camera view. On-device operationalso helps circumvent privacy issues as there are no images being sentto other computer system or server over a network. On-deviceimplementation is also cheaper compared to Cloud version and morereliable if usage of the machine learning system increases (e.g., highCloud usage may cause the server to crash). Although on-deviceimplementation is preferred, Cloud-implementation, server implementation(e.g., implementing the machine learning system on the server describedearlier in the application), and other over-the-network implementations(or a combination of on-device implementation and Cloud-implementation)are also viable if necessary.

The database includes car cards and content cards that each containsvehicle information about a vehicle presented at the auto show.

The step of recognizing or identifying means that the softwareapplication determines that the vehicle in the camera view or imagematches or closely matches a vehicle in the database (a vehicle to beunlocked). The vehicles in the database can be vehicles that arepresented at the auto show or vehicles that are outside of the autoshow.

The computing device is preferably a handheld mobile device so that theuser can carry the mobile device and use the camera on the mobile deviceto identify vehicles as he goes through the auto show. A handheld mobiledevice can be a mobile phone, tablet computer, personal digitalassistant (PDA), cellular device, or other mobile computers that have amicroprocessor and memory. The computing device can also be a desktopcomputer, laptop computer, or other type of devices having amicroprocessor and memory.

The software application is particularly suited for automobiles andautomobile shows, but is also applicable to motorcycles, boats,airplanes, and other types of vehicles and their exhibitions. Thesoftware application can also be used for other objects such as books,furniture, building structures, appliances, or other device. Thesoftware application can also be used to play scavenger hunt games basedon those items.

In some embodiments, the software application is configured to allowusers to use their mobile phone in general such as on a public street todirect to the camera at automobiles or other vehicular transportation.The software application will be configured to use an image from thecamera to identify the vehicle. Preferably, the software application isconfigured to allow the mobile phone to identify make, model, and yearof the model and preferably including any special model series or type(e.g., a special edition model, or limited series model of the vehicle).The software application will retrieve and present vehicle details suchas performance, ratings, engine, size, safety history, or price. It canalso retrieve additional images from a server with the details topresent to the user as an informational capsule about the vehicle. Theuser can then save the capsule for later retrieval and evaluation (orfor sharing with others).

Although in one embodiment the software application is described withrespect to using an image of a vehicle to identify the vehicle, thesoftware application can also work with video. The software applicationcan extract a frame or multiple frames from the video in order to obtainnecessary information to identify the vehicle.

The term image (or frame) can also refer to image data representative ofthe image (or frame data representative of the frame), and vice versa.

Category labels implemented on a softmax layer may refer to categorylabels produced by a softmax layer.

A computing device or server includes a processor and memory (transitorystorage) storing computer executable instructions. The softwareapplication includes computer instructions executable by the processorthat are stored in memory. When the processor executes the instructionsof the software application, the instructions cause the processor toperform the features described above. The computing device or serveralso includes a network interface configured to communicate with acommunications network. A communication network may be Internet, acellular network, a telephone network, a computer network, a packetswitching network, a line switching network, a local area network (LAN),a wide area network (WAN), a global area network, or a private network(Intranet). The network interface is configured to support thecommunications network, allowing one computing device or server toreceive data from and transmit data to another computing device orserver over the communications network. The computing device or serveralso includes non-transitory storage, I/O circuitry, and otherelectronic components.

A server may have larger memory and larger non-transitory storagecapacity than those of a computing device and has the capability ofsustaining concurrent data communication with multiple end users orclient devices. The described handheld mobile device may be a clientdevice.

Database may be implemented by software, hardware, or a combination ofsoftware and hardware. Database may comprise non-volatile media and/orvolatile media. Non-volatile media includes, for example, optical ormagnetic disks, such as storage device. Volatile media includes dynamicmemory, such as main memory. Common forms of storage media include, forexample, a floppy disk, a flexible disk, hard disk, solid state drive,magnetic tape, or any other magnetic data storage medium, a CD-ROM, anyother optical data storage medium, any physical medium with patterns ofholes, a RAM, a PROM, and EPROM, a FLASH-EPROM, NVRAM, any other memorychip or cartridge.

Counterpart method, computer-readable medium, and system embodimentswould be understood from the above and the overall disclosure. Also,broader, narrower, or different combinations of the described featuresare contemplated, such that, for example features can be removed oradded in a broadening or narrowing way.

It will readily be understood by one having ordinary skill in therelevant art that the present invention has broad utility andapplication. Other embodiments may be discussed for additionalillustrative purposes in providing a full and enabling disclosure of thepresent invention. Moreover many embodiments such as adaptations,variations, modifications, and equivalent arrangements will beimplicitly disclosed by the embodiments described herein and fall withinthe scope of the present invention.

Accordingly, while the embodiments of the present invention aredescribed herein in detail in relation to one or more embodiments, it isto be understood that this disclosure is illustrative and exemplary ofthe present invention, and is made merely for the purposes of providinga full and enabling disclosure of the present invention. The detaileddisclosure herein of one or more embodiments is not intended nor is tobe construed to limit the scope of patent protection afforded by thepresent invention, which scope is to be defined by the claims (to bedrafted based on the disclosure) and the equivalents thereof. It is notintended that the scope of patent protection afforded the presentinvention be defined by reading into any claim a limitation found hereinthat does not explicitly appear in the claim itself.

Thus for example any sequence(s) and/or temporal order of steps ofvarious processes or methods (or sequence of system connections oroperation) that are described herein are illustrative and should not beinterpreted as being restrictive. Accordingly, it should be understoodthat although steps of various processes or methods (or connections orsequence of operations) may be shown and described as being in asequence or temporal order, but they are not necessarily limited tobeing carried out in any particular sequence or order. Other sequencesor temporal order is contemplated and would be understood to exist bythose of ordinary skill in the art. In addition systems or featuresdescribed herein are understood to include variations in which featuresare removed, reordered, or combined in a different way.

Additionally it is important to note that each term used herein refersto that which the ordinary artisan would understand such term to meanbased on the contextual use of such term herein. It would be understoodthat terms that have component modifiers are intended to communicate themodifier as a qualifier characterizing the element, step, system, orcomponent under discussion.

The words “may” and “can” are used in the present description toindicate that this is one embodiment but the description should not beunderstood to be the only embodiment.

Although the present invention has been described and illustrated hereinwith referred to preferred embodiments, it will be apparent to those ofordinary skill in the art that other embodiments may perform similarfunctions and/or achieve like results. Thus it should be understood thatvarious features and aspects of the disclosed embodiments can becombined with or substituted for one another in order to form varyingmodes of the disclosed invention.

What is claimed is:
 1. A mobile device for identifying a vehicle at anexhibition event and to obtain information about the vehicle,comprising: a memory device configured to maintain a first databaseincluding target vehicles in the exhibition event and a second databaseincluding prize information; and a processor and non-volatile memorycomprising a set of computer-readable instructions, wherein theprocessor is configured to execute the computer readable instructionsto: communicate with a camera of the mobile device configured to capturea frame; identify an object in the view of the camera, wherein the stepof identifying includes recognizing that the object in the view matchesor closely matches a target vehicle stored in the first database anddisplaying vehicle information of the target vehicle when the identifiedobject matches or closely matches the target vehicle on a screen of themobile device; track a number of preselected vehicles; identify thetarget vehicle to which the object in the view of the camera matches orclosely matches as a preselected vehicle; and present prize informationfrom the second database on the screen of the mobile device according tothe identification of preselected vehicles.
 2. The mobile device ofclaim 1, wherein information of each of the target vehicles stored inthe first database is inaccessible to user of the mobile device beforethe target vehicle is identified by the mobile device as a preselectedvehicle.
 3. The mobile device of claim 1, wherein the processor isfurther configured to execute the computer readable instructions toimplement a control interface configured to control user access to theinformation of each of the target vehicles stored in the first database.4. The mobile device of claim 3, wherein the control interface isconfigured to require user of the mobile device to perform additionalinteractions with the mobile device after the mobile device recognizingthat the object in the view of the camera matches or closely matches atarget vehicle stored in the first database in order to identify thetarget vehicle as preselected vehicle.
 5. The mobile device of claim 1,wherein the processor is further configured to execute the computerreadable instructions to compare the number of preselected vehicles withanother number of preselected vehicles tracked by another mobile device.6. The mobile device of claim 5, wherein the processor is furtherconfigured to execute the computer readable instructions to presentprize information from the second database on the screen of the mobiledevice based on the step of comparison.
 7. The mobile device of claim 1,wherein the processor is further configured to execute the computerreadable instructions to present prize information from the seconddatabase on the screen of the mobile device when a specific targetvehicle is identified as preselected.
 8. The mobile device of claim 1,wherein the step of identifying a vehicle is executed by a machinelearning machine, the machine learning machine including: a detectioncomponent of a pre-trained neural network, wherein the detectioncomponent is configured to detect patterns, edges, shapes, and othercharacteristics of an object in a set of images containing the object,wherein the object is not a vehicle; and a neural network configured toreceive another set of images that each contains a vehicle and relied onthe detection component to learn that each image contains the vehicle.9. The mobile device of claim 1, wherein the step of identifying anobject is executed by a machine learning machine, the machine learningmachine including: a first artificial neural network configured toreceive the frame, detect features of the object in the frame, andproduce a features vector representative of the detected features; and asecond artificial neural network configured to receive the featuresvector and classify the features vector to one of the category labelsimplemented on the second artificial neural network; wherein the machinelearning system is configured to identify the object in the frame basedon the category label to which the features vector is classified. 10.The mobile device of claim 1, wherein the step of identifying an objectis executed by a machine learning machine, the machine learning machineincluding: a first artificial neural network configured to receive theframe, detect features of the object in the frame, and produce afeatures vector representative of the detected features, wherein thefirst artificial neural network is a feature learning neural networkfrom a general task artificial neural network that has been trained withimages from a large dataset, wherein the images from the large datasetinclude no images of the target vehicles; and a second artificial neuralnetwork configured to receive the features vector and classify thefeatures vector to one of the category labels implemented on the secondartificial neural network, wherein the second artificial neural networkis an image classification neural network from a custom neural networkand the image classification neural network has been trained using thefeature learning neural network from a general task artificial neuralnetwork and with images from a small dataset, wherein the images fromthe small dataset include images of the target vehicles.
 11. Anon-transitory computer readable medium storing computer-executableinstructions that causes a mobile device to execute a method foridentifying an object at an exhibition event and to obtain informationabout the objects and unlock prizes, the method comprising: maintainingon the mobile device a first database including target objects in theexhibition event and a second database including prize information,wherein execution of the computer-executable instructions allows aprocessor and memory of the mobile device to: communicate with a cameraof the mobile device configured to capture a frame; identify an objectin the view of the camera, wherein the step of identifying includesrecognizing that the object in the view matches or closely matches atarget object stored in the first database and displaying objectinformation of the target object when the identified object matches orclosely matches the target object on a screen of the mobile device;track a number of preselected objects; identify the target object towhich the object in the view of the camera matches or closely matches aspreselected object; and present prize information from the seconddatabase on the screen of the mobile device according to theidentification of preselected objects, wherein the object, targetobject, and preselected object, are objects of a specific type ofobjects.
 12. The medium of claim 11, wherein information of each of thetarget objects stored in the first database is inaccessible to user ofthe mobile device before the target object is identified by the softwareapplication as a preselected object.
 13. The medium of claim 11, whereinthe execution of the computer-executable instructions allows a processorand memory of the mobile device to further implement a control interfaceconfigured to control user access to the information of each of thetarget objects stored in the first database.
 14. The medium of claim 13,wherein the control interface is configured to require user of themobile device to perform additional interactions with the mobile deviceafter the mobile device recognizing that the object in the view of thecamera matches or closely matches a target object stored in the firstdatabase in order to identify the target object as preselected object.15. The medium of claim 11, wherein the execution of thecomputer-executable instructions allows a processor and memory of themobile device to further compare the number of preselected objects withanother number of preselected vehicles tracked by another mobile device.16. The medium of claim 15, wherein the execution of thecomputer-executable instructions allows a processor and memory of themobile device to further present prize information from the seconddatabase on the screen of the mobile device based on the step ofcomparison.
 17. The medium of claim 11, wherein the execution of thecomputer-executable instructions allows a processor and memory of themobile device to further present prize information from the seconddatabase on the screen of the mobile device when a specific targetobject is identified as preselected.
 18. The medium of claim 11, whereinthe step of identifying an object is executed by a machine learningmachine, the machine learning machine includes: a detection component ofa pre-trained neural network, wherein the detection component isconfigured to detect patterns, edges, shapes, and other characteristicsof an object in a set of images containing the object, wherein theobject is not an object of the specific type of object; and a neuralnetwork configured to receive another set of images that each containsan object of the specific type of object and relied on the detectioncomponent to learn that each image contains the object of the specifictype of object.
 19. The medium of claim 11, wherein the step ofidentifying an object is executed by a machine learning machine, themachine learning machine includes: a first artificial neural networkconfigured to receive the frame, detect features of the object in theframe, and produce a features vector representative of the detectedfeatures; and a second artificial neural network configured to receivethe features vector and classify the features vector to one of thecategory labels implemented on the second artificial neural network;wherein the machine learning system is configured to identify the objectin the frame based on the category label to which the features vector isclassified.
 20. The medium of claim 11, wherein the step of identifyingan object is executed by a machine learning machine, the machinelearning machine includes: a first artificial neural network configuredto receive the frame, detect features of the object in the frame, andproduce a features vector representative of the detected features,wherein the first artificial neural network is a feature learning neuralnetwork from a general task artificial neural network that has beentrained with images from a large dataset, wherein the images from thelarge dataset include no images of the target objects; and a secondartificial neural network configured to receive the features vector andclassify the features vector to one of the category labels implementedon the second artificial neural network, wherein the second artificialneural network is an image classification neural network from a customneural network and the image classification neural network has beentrained using the feature learning neural network from a general taskartificial neural network and with images from a small dataset, whereinthe images from the small dataset include images of the target objects.